Owing to the huge mass of content available over the World Wide Web, end users accessing content provided by service providers are often provided assistance by the service providers, search engines, web publishers, and advertisers in making a selection of content. Conventionally known techniques, such as content based recommendation, collaborative recommendation, etc., are used to generate recommendations to enable such selection by the end users. In content based recommendation, the end users are recommended content, services or products which are similar to the content, services or products used or liked by the end users in the past or which match the interest or choice of the end user. In collaborative recommendation, the end user is recommended content, services or products which are similar to the content, services or products used or liked by other end users having similar or same interest or choices. In an example of content based recommendation, a movie review website may monitor an end user to regularly view a certain category of movies, for example animated movies. Accordingly, every time an animated movie is available for view, the end users may be provided a recommendation, such as a notification or an alert, for example, to download the movie by making relevant payments.
Similarly, in collaborative recommendation, also known as collaborative filtering, service providers may provide targeted advertisements to an end user where these advertisements pertain to product or services that have been preferred by other end users that have similar interests and preferences as the end user. For example, an internet protocol television (IPTV) service provider may recommend television shows or movies to the end user, if the television shows or movies have been viewed by other end users whose interests match the interests of the end user.
In midst of all these techniques for providing assistance and offering relevant content to the end users, users of today are feeling increasingly concerned of their personal and potentially sensitive information. For example, an end user of an e-store may not object to the use of his ordering history or ratings to make anonymous recommendations to other end users, but the end user may not want the other end users to know the particular items that the end user purchased or rated.